View source: R/Get_and_Filter_Regions.R
| plotRegionStats | R Documentation |
plotRegionStats() takes a set of regions from getRegions(),
generates histograms of region characteristics, and saves it as a pdf.
Region-level statistics include width, number of CpGs, minimum coverage, mean
coverage, mean methylation, and methylation standard deviation.
plotRegionStats(
regions,
maxQuantile = 1,
bins = 30,
histCol = "#132B43",
lineCol = "red",
nBreaks = 4,
save = TRUE,
file = "Region_Plots.pdf",
width = 11,
height = 8.5,
verbose = TRUE
)
regions |
A |
maxQuantile |
A |
bins |
A |
histCol |
A |
lineCol |
A |
nBreaks |
A |
save |
A |
file |
A |
width |
A |
height |
A |
verbose |
A |
It's recommended to examine region characteristics before and after filtering.
The vertical line on each histogram indicates the median value for that
feature. A ggplot object is produced and can be edited outside of this
function if desired.
A ggplot object.
getRegions() to generate the set of regions.
plotSDstats(), getRegionTotals(), and plotRegionTotals()
for more help visualizing region characteristics and setting
cutoffs for filtering.
filterRegions() for filtering regions by minimum coverage and
methylation standard deviation.
## Not run:
# Call Regions
regions <- getRegions(bs, file = "Unfiltered_Regions.txt")
plotRegionStats(regions, maxQuantile = 0.99,
file = "Unfiltered_Region_Plots.pdf")
plotSDstats(regions, maxQuantile = 0.99,
file = "Unfiltered_SD_Plots.pdf")
# Examine Region Totals at Different Cutoffs
regionTotals <- getRegionTotals(regions, file = "Region_Totals.txt")
plotRegionTotals(regionTotals, file = "Region_Totals.pdf")
# Filter Regions
regions <- filterRegions(regions, covMin = 10, methSD = 0.05,
file = "Filtered_Regions.txt")
plotRegionStats(regions, maxQuantile = 0.99,
file = "Filtered_Region_Plots.pdf")
plotSDstats(regions, maxQuantile = 0.99,
file = "Filtered_SD_Plots.pdf")
## End(Not run)
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